Shandong University of Science and Technology
Recent publications
Sparse optimization based early fault diagnosis method is drawing more and more attention. In these methods, the objective function is usually a sparsity measure which can represent the impulsive signature produced by the mechanical impacts between rotating components. Therefore, the key point is the fault representation and convergence stability of the objective function. Inspired by this, fast nonlinear blind deconvolution algorithm is proposed for early fault diagnosis of rotating machinery. First, sigmoid function is developed to the generalized form to improve the fault representation ability of the objective function under noisy environment. The nonlinear mapping and sparse expression ability are discussed in detail. Then, Gaussian fitting window function and L1/2 penalty of filter are used to improve the distribution and performance of the weight vector. Finally, simulation data and early bearing fault data are employed to verify the effectiveness and determine optimal parameter setting of the proposed method. Based on the comparison results with the existing methods, the proposed method is found to be a promising method for the early-stage fault diagnosis, which can significantly improve the noise adaptability, computation effectiveness and robustness of fault diagnosis.
NiCo double hydroxide (NiCo-DH) and oxidized activated carbon (OAC) were used to synthesize a three-dimensional (3D) [email protected] via hydrothermal method. OAC acts as the support for in-situ growth of NiCo-DH nanowires, and oxygen-containing groups on the surface of OAC are crucial for the uniform loading of NiCo-DH and keeping structural stability of the composites. Due to the synergistic effect between NiCo-DH and OAC, [email protected] presents an excellent specific capacitance (1990 F g⁻¹ at 1 A g⁻¹). The asymmetric supercapacitor (ASC) is assembled with [email protected] as the positive electrode and OAC as the negative electrode, and exhibits a superior energy density (46.5 W h kg⁻¹ at 800 W kg⁻¹) and a cycling stability (90.7% after 10,000 cycles). Furthermore, ASC has been demonstrated effective in solar panel integrated supercapacitors.
In this study, bismuth oxybromide/carbon (BiOBr/C) visible light catalysts were prepared by using sodium lignosulfonate (SLS) as a surfactant and carbon source to promote the photocatalytic capability of BiOBr. Different amount of SLS was added to regulate the morphology of BiOBr/C. With increasing amount of SLS, the morphology of BiOBr/C was changed from the original flake shape to a microsphere shape, and then self-assembled into a hollow microsphere. The results showed that the as-prepared composites exhibited fairly high removal efficiencies for tetracycline (TC) and hexavalent chromium (Cr(VI)). This was mainly attributed to the variation of morphology and the introduction of carbon, which improved the absorption of light and reduced the band gap for easier separation of photogenerated carriers. Moreover, a possible pathway for the degradation of TC by BiOBr/C was proposed according to the HPLC-MS results. This study provides a novel idea for the application of SLS in the preparation of photocatalysts.
In this paper, we establish the Riemann–Hilbert problem for the non-degenerate high-order solitons of the coupled nonlinear Schrödinger equation. The dynamics of these two components q1 and q2 behave quite differently. By choosing some proper parameters, one component will have a double-hump soliton but the other one will not. Especially, this kind of high-order solitons will not satisfy the scalar nonlinear Schrödinger equation under the SU(2) symmetry. Moreover, we also give the asymptotic analysis of large t for these different components and check the asymptotic solutions and the exact solutions numerically.
Indole and pyrrole that are present in coal tar and fuel oil are representatives of nitrogen heterocyclic neutral compounds (NHNCs). On the one hand, the presence of NHNCs in the hydrodenitrogenation (HDN) process deactivated the catalyst while causing harm to the environment. On the other hand, NHNCs were irreplaceable basic raw materials and intermediates in industry. Therefore, the separation of NHNCs from oil was necessary. In this work, morpholine-based ionic liquids (Mor-based ILs) with low toxicity and biodegradability were considered as green extractants for NHNCs, three Mor-based ILs 1-methyl morpholine p-toluenesulfonate ([C1Mor][TOS]), 1-methyl morpholine benzoate ([C1Mor][BEN]) and 1-butyl-1-methyl morpholinium thiocyanate ([C1C4Mor][SCN]) were synthesized and then characterized by ¹H NMR. All of them were applied to the extraction of NHNCs, and their extraction performance was evaluated by extraction efficiency (EE). The order of the EE for NHNCs was [C1C4Mor][SCN] > [C1Mor][TOS] > [C1Mor][BEN]. Based on this, the extraction conditions of the promising [C1C4Mor][SCN] were optimized, and the EE was 96.89 % and 95.62 % for indole and pyrrole, respectively. Moreover, quantum chemical (QC) calculations were adopted to evaluate the extraction performance at the microscopic nature of the differences, and the theoretical analysis and experiments agreed well with each other. Then the mechanism of NHNCs extraction by [C1C4Mor][SCN] was explored at the molecular level based on density functional theory (DFT). The results showed that NH⋯N hydrogen bonding interaction was formed between anion and NHNCs, while the cation interacted with NHNCs as van der Waals interaction. Finally, the reusability of IL was investigated.
In order to facilitate the synthesis of nickel phyllosilicate through hydrothermal method, the effect of different concentration of Ni(NO3)2 aqueous solutions on nickel phyllosilicate synthesis was investigated in this work. It was found that Ni-phyllosilicate could hardly been formed at the low concentration of the Ni(NO3)2 aqueous solution (0.05 mol・L⁻¹), and a small amount of Ni-phyllosilicate was formed when the concentration increased to 0.10 mol・L⁻¹. The double solvent system of H2O and cyclohexane could further enhance the formation of Ni-phyllosilicate due to the promotion of supersaturation. Furthermore, the N/M-D-A-120 catalyst prepared in the presence of double solvent and the accelerators of NH4F and urea exhibited high Ni content and small Ni crystal size, leading to the high catalytic activity and long-term stability for CO2 methanation. In short, the concentration of Ni(NO3)2 aqueous solution was an important factor for the efficient formation of Ni-phyllosilicate, and the construction of double solvent of H2O and cyclohexane could further increase the supersaturation and enhance the Ni-phyllosilicate synthesis.
Multimodal glioma images provide different features of tumor boundaries based on magnetic resonance imaging (MRI), where multimodal features are often challenging to extract for deep learning segmentation methods. Disturbance between features of different modes is an important factor restricting multimodal learning. To efficiently extract multimodal features, we propose an automatic weighted dilated convolutional network (AD-Net) to learn multimodal brain tumor features through channel feature separation learning. Specifically, the auto-weight dilated convolutional unit (AD unit) utilizes dual-scale convolutional feature maps to acquire channel separation features. We employ two learnable parameters to fuse dual-scale convolutional feature maps in encoding layers, and the two learnable parameters are automatically adjusted with the back propagation of the gradient. We adopt the Jensen–Shannon divergence to constrain the distribution of its feature map, which in turn regularizes the weights of the entire down-sampling. In addition, we use the training technique of deep supervision to achieve fast fitting. Our proposed method got dice scores of 0.90, 0.80, and 0.76 for the whole tumor (WT), the tumor core (TC), and the enhancing tumor (ET) on the BraTS20 dataset. The experimental results showed good performance with the AD-Net network.
Non-orthogonal multiple access (NOMA) has drawn wide attentions in multi-user, resource-constrained mobile systems to improve the system spectral efficiency. In the conventional resource assignment approaches, the optimal power allocation solutions are usually performed among multiple clusters, which, however, incur a high computational complexity due to the diversity of multiple users. In this work, we aim at the practical scenarios of multi-user and multi-cluster NOMA networks with the proposed a low-complexity power allocation and a suboptimal user clustering approach. By using Stackelberg game competition, a closed-form solution with no iterative operations is given first to the power allocation problem. The main idea of the authors’ proposed scheme is to have the base station (BS) allocating resources to all the users freely to guarantee their required minimum transmission-rates, and obtaining the maximal revenue through selling the remaining resources. Then, a suboptimal user clustering approach is further proposed to obtain more revenue for selling the resource. Numerical results show that the proposed power allocation scheme approximates the optimal sum-rates performance, but with lower computational floating-point operations (FLOPs) for the multi-user scene. And, the given user clustering scheme can pursue more revenue of the BS.
Carbon-free hydrogen and ammonia have their own drawbacks when used alone. The composite fuel system formed by the combination of hydrogen and ammonia can not only solve the current energy demand and environmental pollution problems, but also effectively overcome the shortcomings of pure fuel application. Laminar flames are the basis for the study of other flame forms. In this paper, the laminar combustion characteristics of hydrogen/ammonia/air mixture were studied in a constant volume combustor. The initial pressures range from 0.5 to 1.5 atm, the equivalence ratios range 0.5 to 1.5, and the hydrogen ratios range from 0 to 1.0. The laminar burning velocity increases monotonously with the increase of hydrogen ratio, while presents an inverted U-shaped relationship with the equivalence ratio. Compared with the hydrogen ratio and the equivalence ratio, the initial pressure has the weakest effect on the laminar burning velocity of hydrogen/ammonia/air mixture. The laminar burning velocity gradually decreases with the initial pressure. This paper also gives an empirical exponential fitting equation for the laminar burning velocity of hydrogen/ammonia/air mixtures, which can well predict the laminar burning velocity of the mixed gas under various equivalence ratios (0.8–1.2) and various hydrogen ratios (0–1.0) at atmospheric pressure. The influence of fuel composition on the laminar burning velocity should be the result of the combined effect of thermal diffusivity and mass diffusivity. The important fuel consumption pathways R3 and R4 generate more key radicals with increasing hydrogen ratio, thereby promoting the combustion process.
Arrhythmia is the most threatening disease among cardiovascular diseases, and in the last few years, the automatic detection of arrhythmia using neural networks have been intensely focused by physicians. In our work, we propose an effective method to automatically classify electrocardiogram (ECG) signals utilizing residual attention network (RA-NET). RA-NET combines the residual structure and attention mechanism, which can not only generate the attention weight of atrial fibrillation (AF) category to enhance the effective information, but also avoid the network degradation problem in the deep network. Besides, a novel filling algorithm for filling sample values of other recordings with the same category is presented, which is combined with RA-NET to validate model on the PhysioNet Challenge 2017 dataset. According to the comparison with other relevant classification models and filling methods, the experimental results demonstrate that the model we proposed achieves an excellent classification performance, the average of F1-score and sensitivity reach 0.8289 and 0.8955, respectively. For AF category, the precision, F1-score and specificity achieve 0.8763, 0.8835 and 0.9858, separately. With its preeminent performance, the proposed model is capable to play an important auxiliary role in single-lead AF detection.
The complicated mass transfer during coke making process leads to the uneven spatial distribution of sulfur forms in coke, which is of great importance for the sulfur transformation and desulfurization efficiency. In this work, Yanjiahe (YJH) coal with high sulfur content was selected to investigate the spatial distribution and conversion of different sulfur forms during the high temperature coking process by sulfur K-edge X-ray absorption near edge structure (XANES) spectroscopy. In consideration of volatile matters evolution pathway, the content and transformation of different sulfur forms at different positions of the coke during pyrolysis were determined. The results show that the desulfurization rate of coke obtained at 500 °C gradually increases with the migration direction of volatiles. The sulfur-containing gases generated at the bottom of coke is difficult to escape due to the resistance of coke at the top, so the secondary reaction with coke in the bottom causes the increase of total sulfur content. In addition, the thiophenic sulfur content at the top is lower than that of bottom or center. This indicates that the volatiles generated by the decomposition of thiophenic sulfur at the top more easily escape from coke than that of bottom or center. When the pyrolysis temperature continues to rise, the desulfurization rate decreases with the migration direction of volatiles. The sulfoxide at the top can be converted into sulfonate and sulfate, so the sulfoxide content at the top is less than that in the bottom while the sulfonate and sulfate content is the highest at the top than that in the bottom. High temperature is promotive to the decomposition of organic sulfur or inorganic sulfur, and to promote the combination of the –OH groups or -H groups and sulfur radicals, which results in release of SO2 or H2S. The various sulfur forms perform different migration pathway with temperatures from the bottom to the top of the coke.
In view of the complex characteristics of PPP project risk factors under the background of new infrastructure construction, this paper proposes a risk evaluation model based on improved particle swarm optimization-Fuzzy Analytic Hierarchy Process (PSO-FAHP); the fuzzy complementary judgment matrix of Analytic Hierarchy Process Consistency has always been a difficult problem in academia. This paper introduces particle swarm algorithm to transform the core content of FAHP-consistency judgment into objective function and constraint conditions, so that the weight result is more accurately revised on the basis of the maximum retention of expert judgment. In the end, this method was introduced into the new infrastructure PPP project example and found that although the newly added risk indicator—project management is inferior to construction risk and operation risk, its criticality cannot be ignored. KeywordsNew infrastructure constructionPSO-FAHPPPPRisk
Direct coal liquefaction (DCL) is considered to be the effective method for production of aviation fuel and valuable chemicals, while low rank coal is appropriate feedstock because of high thermal reactivity. Original hydrogen bonds could promote low-temperature crosslinking reactions. liquefaction solvents could partially break original hydrogen bonds by forming new species. Impacts of hydrogen bonds formed by solvents on DCL reactions have rarely been studied. In order to figure out role of hydrogen bonds in DCL, a conjoint study of DCL experiments and density functional theory (DFT) calculations was carried out. More carboxyl groups were promoted to decompose. Meanwhile more aldehyde groups were found in solid products with increase of liquefaction temperature. As aldehyde groups are generated, hydrogen consumption increases. In terms of mechanism, effect of stronger hydrogen bonds on cleavage of covalent bonds were investigated by DFT-based potential energy surface scan. It turns out that cleavage of CO bonds in carboxyl groups could be enhanced, so that stabilization of free radicals results in consumption of extra hydrogen, which is provided by tetralin. Results of experimental methods are consistent with DFT calculations. The catalytic mechanism of hydrogen bonds upon conversion of carboxyl groups during DCL is proposed. Hydrogen bonds between coal and liquefaction solvents promote decomposition of carboxyl groups and consequently improve hydrogen transfer and thermal reactivity of solid products. Consequently, hydrogen bonds modulation should be taken into account during preparation of DCL solvents, for suppression of crosslinking reactions.
Catalytic hydrogenation of CO2 to light olefin (C2= - C4=) not only reduces greenhouse gas emissions but also utilizes CO2 efficiently. A highly active and selective iron-based composite from facile high-energy mechanochemical ball milling was developed for CO2 hydrogenation to C2= - C4=. Under a condition of 320 ℃, 1.0 MPa and 9600 mL h⁻¹ gcat⁻¹, a high C2= - C4= selectively of 55.4 % in hydrocarbons at CO2 conversion of 32.1 % is achieved, outperforming the previous reports under similar reaction condition. An “O-Fe/Mg-O” structure formed by incorporating of Mg into Fe3O4 surface during high-energy mechanochemical ball milling contributes to the efficient adsorption and activation of CO2 to CO. The sustainable CC coupling of CO with surface carbon on K2O-adsorbed Fe5C2 surface promotes the C2= - C4= production. This work provides a new strategy for developing highly efficient catalyst for CO2 conversion to value-added chemicals.
This study aims to determine the prevention mechanisms of spontaneous coal combustion in thermosensitive composite hydrogels. A thermosensitive composite hydrogel is prepared using methylcellulose, polyethylene glycol, and sodium chloride. A self-designed fire extinguishing experimental device is used to test the inhibitory effects of the thermosensitive composite hydrogel on spontaneous coal combustion. Changes in the water loss rate of the hydrogel under different temperature conditions are also studied. The surface morphology and pore structure of the thermosensitive composite hydrogel are observed by scanning electron microscopy. Finally, the characteristics of the functional groups during the combustion of the coal samples are analysed using a Fourier transform infrared spectrometer. The results show that the thermosensitive composite hydrogel undergoes a phase transformation at temperatures above 58 °C. When the coal sample temperature reaches 200 °C, the cooling and suffocation effects of the hydrogel are optimal because the water loss rate of the hydrogel exceeds 50 %. Furthermore, the hydrogel is firmly coated on the surface of the coal body to prevent any contact between coal and oxygen. In addition, the thermosensitive composite hydrogel generates a strong dehydrating agent and inert gas after thermal decomposition. The collision between gas products and free radicals reduces the effective collision probability between the coal surface active molecules and oxygen, further inhibiting spontaneous coal combustion. These results provide a basis for the practical application of thermosensitive composite hydrogels in underground mines, and guidance for the research and development of thermosensitive composite hydrogel materials.
The enhancement of efficiency converting microwave energy into heat has become great concern on industrial application of microwave heating. Microwave discharge can generate more hot spots, exhibiting more intense thermal effect than microwave heating alone. Microwave discharge can be achieved by a variety of carbon-based dielectrics like bio-char, whose performances are highly dependent on graphite degree of the carbon-based dielectrics. Therefore, graphite addition into bio-char by mechanical mixing is promising to improve the discharge performance. However, there are few studies on characteristics of microwave-induced discharge by spherical bio-char with graphite addition, and kinetic analysis of microwave discharge is lacking. Moreover, the mechanism of microwave-induced discharge of spherical carbon-based dielectrics has not been fully clarified. As a result, the work firstly studied characteristics of microwave-induced discharge of spherical bio-char with graphite addition. The other focus of this work was to explore mechanism of microwave-induced discharge through electric field simulation. The results revealed the average discharge intensity of spherical graphite in the first 20 min was 38.2 % higher than that of spherical bio-char. Microwave-induced discharge of bio-char with graphite addition had synergistic effect at the initial stage. It was obtained apparent activation energies of microwave-induced discharge of bio-char and graphite were 237.08 W·g⁻¹ and 48.42 W·g⁻¹, respectively. The formation of electric field polarization focuses offered active sites for microwave discharge. The work is of significance for fundamental research about microwave-induced discharge of carbon-based dielectrics.
The involvement of highly chemically reactive H2 greatly increases the fuel explosion risk. In order to reveal the elevated explosion risk of LPG/DME blended gas due to the participation of H2, the explosion-promoting dynamics of H2 and its sensitivity characteristics are investigated based on a combination method of numerical simulation, experimental verification and theoretical analysis. The results show that the participation of H2 increases the explosion overpressure and the shockwave propagation velocity by 147.9% and 90% respectively to the greatest extent. The explosion temperature shows five typical combustion stages, with the participation of H2, the maximum temperature rise range of the shock wave adiabatic compression heating period and the combustion period is significantly increased, the adiabatic holding time is prolonged, the peak flame temperature is increased, and the flame propagation velocity is also significantly improved. The addition of H2 causes the relative distance between the explosion shock wave and the flame front first increases, then decreases and then increases in the shape of “half-morning glory”. The number and sensitivity coefficients of the new explosion-promoting elementary reactions brought by H2 are larger than that of the new explosion-inhibiting elementary reactions, which explains and confirms the explosion-promoting nature of H2 in terms of the reaction mechanism.
Cyclohexane-insoluble portion (CHISP), ethanol-insoluble portion (EISP) and isopropanol-insoluble portion (IPISP) were obtained by sequential thermal dissolution (STD) from Naomaohu lignite (NL), with cyclohexane (CH), ethanol (E) and isopropanol (IP) as the solvent. The insoluble portions were characterized by Ultimate analysis, Scanning electron microscope (SEM), X-ray photoelectron spectrometer (XPS), Fourier transform infrared spectrometer (FTIR), Thermogravimeter (TG-DTG) and Pyrolysis-chromatography/mass spectrometry (Py-GC/MS). As a result, compared to NL, the gradually increased of C element and decreased of O, N and S in the insoluble portion (ISP) demonstrated that STD is a carbon-rich and heteroatom removal process. The yield of CHISP reached 98.64 % and exhibited smooth surface. The existence of –COO– species in EISP and IPISP indicated that E and IP might be esterified or ester-exchanged with carboxylic acid, while the reduced >CO species illustrated that the nucleophilic oxygen atom in E and IP attacked the carbon atom on the carbonyl group in NL. The comprehensive structural parameter (CSPM) and comprehensive volatile release characteristics index (CVRCI) of NL, CHISP, EISP and IPISP were calculated by FTIR and TG-DTG, respectively. Results showed that STD process improves pyrolysis reactivity, and the CSPM was directly related to CVRCI, presenting a linear relationship (R²=0.952). That is, CSPM can be used as an important parameter to predict the pyrolysis reactivity (CVRCI) of coal. Py-GC/MS showed that, compared to NL, the number of group component was increased in CHISP and decreased in EISP and IPISP during the sequential thermal dissolution process, indicating that there might be non-covalent bond break and formation during the process
Premature beats are a cardiovascular disease, which can lead to complications of other diseases. An electrocardiograph (ECG) is the main tool for detecting premature beats, but accurate detection of premature beats still faces challenges. This work designs a single-scale convolution wavelet feature optimization (SSCWFO) classification model that is based on a single-scale convolutional neural network (SSCNN) model and optimized by Linear Discriminant Analysis (LDA) algorithm. First, the wavelet coefficient features are extracted from 12-lead ECG signals using Daubechies5, and then the 5th level detail coefficient features are transformed into images using Gramian Angular Difference Fields (GADFs). Thereafter the single-scale features are extracted by the SSCNN model. Next, the LDA algorithm was used to maximum inter-class distance and the minimum intra-class distance of each category. Finally, the Gaussian naive Bayes is used to classify-three types of signals. The results after LDA optimization show that the accuracy, precision, sensitivity, F1-score, and area under the ROC curve (AUC) of the SSCWFO model are improved to 91.12%, 92.29%, 92.55%, 92.13%, and 0.9197, respectively. Also, the network architectures of Resnet34, Resnet18, and LeNet 5 are used in this work for a comparison; their accuracy is 75.93%, 76.88%, and 68.48%, respectively. This shows that the method can effectively distinguish normal signals and two types of premature beat diseases and is helpful to the effective classification of diseases of premature beats.
In this study, two Ca/Mn/Fe perovskite oxygen carriers (i.e., CaMn0.5Fe0.5O3−δ and CaMn0.75Fe0.25O3−δ, respectively) were prepared using red mud and two pure reagents to recycle the red mud for the chemical looping combustion of biomass. The oxygen uncoupling (OU) capacity and redox cycle characteristics of perovskites were examined by thermogravimetric analysis (TGA) under a gaseous (H2, O2, N2) environment. With the same Mn/Fe mole ratio, reagent-based perovskites exhibited a higher total oxygen transport capacity, while perovskites derived from red mud exhibited a better cycling stability. The addition of red mud did not affect the reactivity of perovskites. The curves of conversion level vs time revealed that two control mechanisms are involved in the reaction between oxygen carriers and gaseous compounds. That is, the oxidation kinetics of perovskite oxygen carriers were mainly controlled by a chemical reaction, while the reduction kinetics were controlled by a chemical reaction at a low conversion level, and with the increase in the reduction degree, gas diffusion through the solid product layer was enhanced. In addition, kinetic parameters of OU and redox were obtained, and the grain model was considered to be suitable to describe reduction and oxidation reactions.
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1,000 members
Yunliang Tan
  • College of Mining & Safety Engineering
Baogui Xin
  • College of Economics and Management
Chang-Fu Zhou
  • College of Earth Science and Engineering
Huanqing Cui
  • Department of Software Engineering, College of Computer Science and Engineering
Shu-Chuan Chu
  • College of Computer Science and Engineering
579 Qianwangang Road Economic & Technical Development Zone, 266590, Qingdao, China
Head of institution
Qingguo Yao